Irrigation and Water Engineering

Irrigation and Water Engineering

Modeling groundwater quality parameters using geostatistical models in Markazi Province

Document Type : Original Article

Authors
mojde minaei 1
Hossein Aghamohammadi 2
Mohammad H. Vahidnia 3
Amin R. Neshat 4
Saeed Behzadi 5
1 PhD Student at Department of Natural resources and environment, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Assistant Professor at Department of Natural resources and environment and GIS, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 Assistant Professor at Center for Remote Sensing and GIS Research, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
4 Assistant Professor at Department of Natural resources and environment, Science and Research Branch, Islamic Azad University, Tehran, Iran.
5 Associate Professor, Department of Surveying Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran
10.22125/iwe.2025.536879.1892
Abstract
Population growth, industrial expansion, and improved living standards have significantly increased water demand in Iran. As the main source of supply in arid and semi-arid regions, groundwater plays a vital role in agriculture, industry, and drinking water. Assessing its quality is therefore essential, particularly in Markazi Province. Traditional evaluation methods are often costly and time-consuming, highlighting the need for mathematical and geostatistical approaches for mapping and prediction.



This study analyzed the spatial and temporal variations of three key groundwater quality parameters—total dissolved solids (TDS), sulfate, and sodium—in Markazi Province during 2017, 2020, and 2023. These parameters strongly influence water salinity and hardness, making them critical for determining water usability. Data were obtained from the Water and Wastewater Company of Markazi Province. Universal Kriging was applied with three semivariogram models (exponential, Gaussian, and spherical), and model accuracy was evaluated using RMSE, MAE, and R² indices.



Results indicated that the Gaussian model outperformed the others for predicting all three parameters in every study year. Higher average concentrations in 2020 suggested increased contamination in some samples, whereas maps from 2023 revealed a general improvement in water quality across the region.



Overall, the findings confirm the strong potential of geostatistical methods in groundwater quality assessment and highlight their value as effective tools for sustainable groundwater management in vulnerable regions.
Keywords
groundwater quality
universal kriging
TDS
Sulfate
Sodium
Subjects
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